Climate change and urbanization causes the increasing challenges of flooding in urban watersheds. Even the rivers identified as non-vulnerable are causing catastrophic damage due to heavy flooding. So, several satellite and radar-based precipitation data are considered to study the watersheds with no gauge station or need recent precipitation data. Weather Radar (NEXRAD)arch, the accuracy of satellite-based precipitation data, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), and radar-based precipitation data, Next Generation Weather Radar (NEXRAD), is evaluated in rainfall-runoff simulation considering Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Personal Computer Storm Water Management Model (PCSWMM), respectively. The primary research proposes a framework for modeling the rainfall-runoff process using PERSIANN-CDR and a floodplain map in an ungauged urban watershed. The one-dimensional Hydrologic Engineering Centre-River Analysis System (HEC-RAS) model generates a flood inundation map for the pertinent flooding occurrences from the acquired peak hydrograph, providing a quantifiable display of the inundation extent percentage. The second research uses the PCSWMMs to show the extent of flooding. It also employs the compromise programming method (CPM) to rank the most critical sub-catchments based on three parameters: slope, surface area, and impervious area. Three low-impact development (LID) strategies over the watershed determine the best flood management option. Therefore, the overall study presents a comprehensive framework for flood management in urban watersheds that integrates satellite precipitation data, hydrologic modeling, and LID strategies. The framework can provide an accurate flood-prone zone and help prioritize critical sub-catchments for flood management options. The study proposes using HEC-HMS and PCSWMM models to simulate and analyze interactions between rainfall, runoff, and the extent of the flood zone. Furthermore, LID can be applied to reduce flooding in urban watersheds. Overall, the framework can be helpful for policymakers and system managers to build the watershed's resilience during catastrophic flooding events caused by climate change and urbanization.

Hydrology Modeling using HEC-HMS (Hydrological Engineering Centre-Hydrologic Modeling System) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, Machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research's high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. In addition, Point gauge observations have historically been the primary source of the necessary rainfall data for hydrologic models. However, point gauge observation does not provide accurate information on rainfall's spatial and temporal variability, which is vital for hydrological models. Therefore, this study also evaluates the performance of satellite and radar precipitation products for hydrological analysis. The results revealed that integrated Machine Learning and physical-based model could provide more confidence in rainfall-runoff and flood depth prediction. Similarly, the study revealed that radar data performance was superior to the gauging station's rainfall data for the hydrologic analysis in large watersheds. The discussions in this research will encourage researchers and system managers to improve current rainfall-runoff simulation models by application of Machine learning and radar rainfall data.

With contributions from a panel of researchers from a wide range of fields, the chapters of this book focus on evaluating the potential, utility and application of high resolution satellite precipitation products in relation to surface hydrology.

Precipitation is one of the most important components contributing to hydrological dynamics. Spatially distributed precipitation data can be obtained by satellite, radar, rain gages, etc, to serve various purposes. Currently, the most commonly used precipitation data still rely on gage-based measurement techniques that provide timely precipitation information with high quality and reliability. The National Oceanic and Atmospheric Administration (NOAA) and its cooperative climate stations are the primary resources of this form of precipitation data at the federal level. For hydrological simulation of precipitation-runoff for a watershed, precipitation is a critical model input that has a significant impact on the certainty and accuracy of simulation. To better understand the hydrological process within Whitewater River Basin in Kansas, the Precipitation-Runoff Model System (PRMS) was applied to this area, where the Cooperative Atmosphere-Surface Exchange Study (CASES) has set up an intensively instrumented site managed by Hydrologic Science Team (HST) of Oregon State University for rainfall data collection. Two rainfall data sources, NOAA and HST, were used in this study to simulate the stream response to rainfall within the basin. Different simulation results were acquired compared and analyzed. The study concluded that better simulation results were obtained with MMS-PRMS using integrated spatially distributed precipitation data, which was not available as a standard NOAA product. For a large basin, it is necessary to collect precipitation data within the area of interest in addition to standard NOAA data to produce an accurate hydrological model. It was suggested that to guarantee the quality of precipitation-runoff simulation using MMS-PRMS, the coverage of each rain gage should not be larger than 40 to 50 square kilometers (about 15-20 square miles). It was also learned that the precipitation data from local supplementary measurements are unlikely to be a satisfactory substitute for current NOAA data in hydrological simulation due to the short time period of measurement. The combination of standard NOAA data and additional data from an intensively measured site, such as CASES, or from radar, would allow more for better simulation.

No other book can offer such a powerful tool to understand the basics of remote sensing for precipitation, to make use of existing products and to have a glimpse of the near future missions and instruments. This book features state-of-the-art rainfall estimation algorithms, validation strategies, and precipitation modeling. More than 20 years after the last book on the subject the worldwide precipitation community has produced a comprehensive overview of its activities, achievements, ongoing research and future plans.